Process Mining Meets Abstract Interpretation

Carmona, Josep; Cortadella, Jordi

doi:10.1007/978-3-642-15880-3_18

Cited by 28 publications

(24 citation statements)

References 17 publications

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“…Dozens of process discovery [2,6,7,11,13,18,19,21,24,29,33,35,36] and conformance checking [3, 8-10, 16, 22, 24, 30-32, 34] approaches have been proposed in literature. Despite the growing maturity of these approaches, the quality and efficiency of existing techniques leave much to be desired.…”

Section: Introductionmentioning

confidence: 99%

Decomposing Process Mining Problems Using Passages

Aalst

2012

Lecture Notes in Computer Science

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Abstract. Process discovery-discovering a process model from example behavior recorded in an event log-is one of the most challenging tasks in process mining. Discovery approaches need to deal with competing quality criteria such as fitness, simplicity, precision, and generalization. Moreover, event logs may contain low frequent behavior and tend to be far from complete (i.e., typically only a fraction of the possible behavior is recorded). At the same time, models need to have formal semantics in order to reason about their quality. These complications explain why dozens of process discovery approaches have been proposed in recent years. Most of these approaches are time-consuming and/or produce poor quality models. In fact, simply checking the quality of a model is already computationally challenging. This paper shows that process mining problems can be decomposed into a set of smaller problems after determining the so-called causal structure. Given a causal structure, we partition the activities over a collection of passages. Conformance checking and discovery can be done per passage. The decomposition of the process mining problems has two advantages. First of all, the problem can be distributed over a network of computers. Second, due to the exponential nature of most process mining algorithms, decomposition can significantly reduce computation time (even on a single computer). As a result, conformance checking and process discovery can be done much more efficiently.

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Section: Introductionmentioning

confidence: 99%

Decomposing Process Mining Problems Using Passages

Aalst

2012

Lecture Notes in Computer Science

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“…Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. For example, dozens of process discovery [1,9,11,16,32,18,22,23,27,33,39,48,51,52] and conformance checking [6,13,14,15,21,28,33,41,42,47,50] approaches have been proposed in literature. However, this paper is not about new processmining techniques but about getting the event data needed for all of these techniques.…”

Section: Related Workmentioning

confidence: 99%

Extracting Event Data from Databases to Unleash Process Mining

Aalst

2015

Management for Professionals

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Abstract. Increasingly organizations are using process mining to understand the way that operational processes are executed. Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards "better" processes. Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However, process mining stands or falls with the availability of event logs. Existing techniques assume that events are clearly defined and refer to precisely one case (i.e. process instance) and one activity (i.e., step in the process). Although there are systems that directly generate such event logs (e.g., BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly. However, when creating or using process models "raw data" need to be linked to cases and activities. This paper uses a novel perspective to conceptualize a database view on event data. Starting from a class model and corresponding object models it is shown that events correspond to the creation, deletion, or modification of objects and relations. The key idea is that events leave footprints by changing the underlying database. Based on this an approach is described that scopes, binds, and classifies data to create "flat" event logs that can be analyzed using traditional process-mining techniques.

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“…These formalisms are widely used within process mining as results of application of process discovery algorithms [9,10,12,16,20,26,36]. Having conversion algorithms to BPMN format will give an opportunity to discover control flow models, which could be integrated with other process perspectives.…”

Section: Converting Process Models Into Bpmnmentioning

confidence: 99%